‘The ideal number of human employees inside of any company is zero’: Why AI gives company owners what they think they want

· · 来源:user资讯

Пьяный турист нанес тяжелую травму участвовавшей в Олимпиаде сноубордистке20:38

軍方系統是在本屆人大中被撤換與辭任最多的系統,無論是絕對數量還是比例。 若今年解放軍代表團明顯縮小,或多名理論上仍具人大席位的軍方高層未出席,這不僅是反腐進展的旁證,也會被視為習近平繼續重塑軍隊指揮鏈、壓制潛在派系摩擦的信號。

让传承人吃饱饭91视频对此有专业解读

Abstract:Humans shift between different personas depending on social context. Large Language Models (LLMs) demonstrate a similar flexibility in adopting different personas and behaviors. Existing approaches, however, typically adapt such behavior through external knowledge such as prompting, retrieval-augmented generation (RAG), or fine-tuning. We ask: do LLMs really need external context or parameters to adapt to different behaviors, or do they already have such knowledge embedded in their parameters? In this work, we show that LLMs already contain persona-specialized subnetworks in their parameter space. Using small calibration datasets, we identify distinct activation signatures associated with different personas. Guided by these statistics, we develop a masking strategy that isolates lightweight persona subnetworks. Building on the findings, we further discuss: how can we discover opposing subnetwork from the model that lead to binary-opposing personas, such as introvert-extrovert? To further enhance separation in binary opposition scenarios, we introduce a contrastive pruning strategy that identifies parameters responsible for the statistical divergence between opposing personas. Our method is entirely training-free and relies solely on the language model's existing parameter space. Across diverse evaluation settings, the resulting subnetworks exhibit significantly stronger persona alignment than baselines that require external knowledge while being more efficient. Our findings suggest that diverse human-like behaviors are not merely induced in LLMs, but are already embedded in their parameter space, pointing toward a new perspective on controllable and interpretable personalization in large language models.

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But analysts said the risk that a sustained leap in oil prices could put upward pressure on prices may give policymakers pause for thought.